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In production systems mostly when working in microservices architecture, it is soon realized that monitoring each service individually is often not enough to troubleshoot complex issues. It becomes a necessity to have a full picture of the whole call stack through the entire application with detailed information on service topologies, network latencies and individual request durations. This is usually where distributed tracing comes to the rescue. In this post, the concept of distributed tracing will be introduced in microservices architecture.
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Since releasing our open-source Istio operator, we’ve been doing our best to add support for the latest versions of Istio as rapidly as possible. Today, we’re happy to announce that we have added Istio 1.3 support for the Banzai Cloud Istio operator. In this post, we’ll be outlining how to easily upgrade Istio control planes to 1.3 with the Banzai Cloud Istio operator, within a single-mesh multi-cluster topology or across a multi-cloud or hybrid-cloud service mesh.
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Istio has been rightfully praised for ushering in free observability and secure service to service communication. Other, more significant features, however, are what truly make Istio the Swiss army knife of service mesh operators; when it comes to meeting SLOs like uptime, latency and error rates, the ability to manage traffic between services is absolutely critical. When we released the Istio operator earlier this year, our goal (besides managing Istio installation and upgrades) was to provide support for these excellent traffic routing features, while making everything more usable and UX friendly.
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At Banzai Cloud we work with Istio quite a bit and run a lot of Istio-based service meshes for our customers. Earlier this year we opensourced the Banzai Cloud Istio operator in order to simplify provisioning, management, upgrades, and multi-cluster scenarios. However, we still felt that this was not simple enough. A few months ago we announced Backyards, a method of simplifying, creating and managing Istio, with planned support for all Istio features - the more complicated, the better!
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Many organizations are embracing a more decentralized decision making process, allowing them to better build cloud native applications, and to deliver them more rapidly and safely. Often, different teams manage different microservices, with each team able to release their microservices independent of the others. Istio is exceedingly helpful for this, since it provides low level contructs able to enforce policies in the network and configure traffic control, observability, rate limiting, circuit breaking and programmable rollouts.
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Since releasing our open-source Istio operator, we’ve been doing our best to add support for the latest Istio versions as rapidly as possible. Today, we’re happy to announce that we have added Istio 1.2 support for the Banzai Cloud Istio operator. When we added Istio 1.1 support for the operator, we wrote a detailed blog post about how to employ a seamless Istio control plane upgrade in a single-mesh, single-cluster setup.
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A few weeks ago we announced Backyards, Banzai Cloud’s automated service mesh built on top of our Istio operator, which greatly simplifies the complex management of service meshes across multi and hybrid-cloud environments. Backyards is integrated into Banzai Cloud’s container management platform, Pipeline. However, it also works, and is available, as a standalone product. Naturally, using Backyards with Pipeline provides users with a variety of specific benefits (like managing applications in a multi-cloud world) but Backyards works on any Kubernetes installation.
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In the last few months we wrote a lot of different blog posts about the Istio service mesh. We started with a simple Istio operator, then went on with different multi-cluster service mesh topologies, Istio CNI and a telemetry deep dive. The contents of the posts were built around our open source Istio operator that helps installing and managing an Istio service mesh in a single or multi and hybrid-cluster setup.
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Service mesh probably needs no introduction. But, just to recap, let’s define it as a highly configurable, dedicated and low‑latency infrastructure layer designed to handle and provide reliable service-to-service communication, implemented as lightweight network proxies deployed alongside application code. Typical examples of mesh services are service discovery, load balancing, encryption, observability (metrics and traces) and security (authn and authz). Circuit breakers, service versioning, and canary releases are frequent use cases, all of which are part of any modern cloud-native microservice architecture.
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One of the core features of the Istio service mesh is the observability of network traffic. Because all service-to-service communication is going through Envoy proxies, and Istio’s control plane is able to gather logs and metrics from these proxies, the service mesh can give you deep insights about your network. While a basic Istio installation is able to set up all the components needed to collect telemetry from the mesh, it’s not easy to understand how these components fit together and how to configure them in a production environment.
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